rust-async-internals
Rust async internals skill for understanding and debugging async Rust. Use when understanding the Future trait and poll model, Pin and Unpin, tokio task scheduling, debugging async stack traces with tokio-console, tracking waker leaks, using select! and join!, or avoiding blocking in async contexts. Activates on queries about Rust async internals, Future poll, Pin, Unpin, tokio-console, waker, async stack traces, select!, join!, or blocking in async.
What this skill does
# Rust Async Internals
## Purpose
Guide agents through Rust async/await internals: the `Future` trait and poll loop, `Pin`/`Unpin` for self-referential types, tokio's task model, diagnosing async stack traces with tokio-console, finding waker leaks, and common `select!`/`join!` pitfalls.
## Triggers
- "How does async/await actually work in Rust?"
- "What is Pin and Unpin in async Rust?"
- "My async code is slow — how do I profile it?"
- "How do I use tokio-console to debug async tasks?"
- "I have a blocking call in async — what do I do?"
- "How does select! work and what are the pitfalls?"
## Workflow
### 1. The Future trait — poll model
```rust
// std::future::Future (simplified)
pub trait Future {
type Output;
fn poll(self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Self::Output>;
}
pub enum Poll<T> {
Ready(T), // computation done, T is the result
Pending, // not ready yet, waker registered, will be polled again
}
```
Execution model:
1. Calling `.await` calls `poll()` on the future
2. If `Pending`: current task registers its waker and yields to the runtime
3. When the waker is triggered (I/O ready, timer fired), the runtime re-polls
4. If `Ready(val)`: the `.await` expression evaluates to `val`
### 2. Implementing a simple Future
```rust
use std::{
future::Future,
pin::Pin,
task::{Context, Poll},
time::{Duration, Instant},
};
struct Delay { deadline: Instant }
impl Delay {
fn new(dur: Duration) -> Self {
Delay { deadline: Instant::now() + dur }
}
}
impl Future for Delay {
type Output = ();
fn poll(self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<()> {
if Instant::now() >= self.deadline {
Poll::Ready(())
} else {
// Register the waker — runtime calls waker.wake() to re-poll
// In production: register with I/O reactor or timer wheel
let waker = cx.waker().clone();
let deadline = self.deadline;
std::thread::spawn(move || {
let now = Instant::now();
if deadline > now {
std::thread::sleep(deadline - now);
}
waker.wake(); // notify runtime to re-poll
});
Poll::Pending
}
}
}
// Usage
async fn main() {
Delay::new(Duration::from_secs(1)).await;
println!("Done");
}
```
### 3. Pin and Unpin
`Pin<P>` prevents moving the value behind pointer `P`. This matters because async state machines contain self-referential pointers (a reference into the same struct where the future lives):
```rust
// Why Pin is needed: async fn compiles to a state machine struct
// that may have self-references across await points
async fn example() {
let data = vec![1, 2, 3];
let ref_to_data = &data; // reference into same stack frame
some_async_op().await; // suspension point
println!("{:?}", ref_to_data); // reference still used after suspend
}
// The state machine stores both `data` and `ref_to_data`.
// If the struct were moved, `ref_to_data` would dangle.
// Pin<&mut State> prevents moving the state machine.
// Unpin: a marker trait for types that are safe to move even when pinned
// Most types implement Unpin automatically
// Futures generated by async/await do NOT implement Unpin
// Creating a Pin from Box (heap allocation → safe)
let boxed: Pin<Box<dyn Future<Output = ()>>> = Box::pin(my_future);
// Pinning to stack (unsafe, use pin! macro)
use std::pin::pin;
let fut = pin!(my_future);
fut.await; // or poll it directly
```
### 4. tokio task model
```rust
use tokio::task;
// Spawn a task (runs concurrently on the runtime thread pool)
let handle = tokio::spawn(async {
// ... async work ...
42
});
let result = handle.await.unwrap(); // wait for completion
// spawn_blocking — for CPU-bound or blocking I/O
let result = task::spawn_blocking(|| {
// runs on a dedicated blocking thread pool
std::fs::read_to_string("big_file.txt")
}).await.unwrap();
// yield to runtime (cooperative multitasking)
tokio::task::yield_now().await;
// LocalSet — for !Send futures (single-threaded)
let local = task::LocalSet::new();
local.run_until(async {
task::spawn_local(async { /* !Send future */ }).await.unwrap();
}).await;
```
### 5. tokio-console — async task inspector
```toml
# Cargo.toml
[dependencies]
console-subscriber = "0.3"
tokio = { version = "1", features = ["full", "tracing"] }
```
```rust
// main.rs
fn main() {
console_subscriber::init(); // must be called before tokio runtime
tokio::runtime::Builder::new_multi_thread()
.enable_all()
.build()
.unwrap()
.block_on(async_main());
}
```
```bash
# Install tokio-console CLI
cargo install --locked tokio-console
# Run your app with tracing enabled
RUSTFLAGS="--cfg tokio_unstable" cargo run
# In another terminal, connect tokio-console
tokio-console
# tokio-console shows:
# - Running tasks with their names, poll times, and wakeup counts
# - Slow tasks (high poll duration = blocking in async!)
# - Tasks that have been pending for a long time (stuck?)
# - Resource contention (mutex/semaphore wait times)
```
### 6. Blocking in async — common mistake
```rust
// WRONG: blocking call in async context blocks entire thread
async fn bad() {
std::thread::sleep(Duration::from_secs(1)); // blocks runtime thread!
std::fs::read_to_string("file.txt").unwrap(); // blocking I/O blocks runtime!
}
// CORRECT: use async equivalents
async fn good() {
tokio::time::sleep(Duration::from_secs(1)).await; // async sleep
tokio::fs::read_to_string("file.txt").await.unwrap(); // async I/O
}
// CORRECT: if you must block, use spawn_blocking
async fn with_blocking() {
let content = tokio::task::spawn_blocking(|| {
heavy_cpu_computation() // runs on blocking thread pool
}).await.unwrap();
}
```
### 7. select! and join! pitfalls
```rust
use tokio::select;
// select! — complete when FIRST branch completes, cancels others
select! {
result = fetch_a() => println!("A: {:?}", result),
result = fetch_b() => println!("B: {:?}", result),
// Pitfall: the LOSING branches are DROPPED immediately
// If fetch_a wins, fetch_b's future is dropped (and its state machine cleaned up)
// This is correct and safe — but can be surprising
}
// join! — wait for ALL to complete
let (a, b) = tokio::join!(fetch_a(), fetch_b());
// Biased select (always check first branch first)
loop {
select! {
biased; // prevents fairness, checks in order
_ = shutdown_signal.recv() => break,
msg = queue.recv() => process(msg),
}
}
// select! with values from loop (use fuse)
let mut fut = some_future().fuse(); // FusedFuture: safe to poll after completion
loop {
select! {
val = &mut fut => { /* ... */ break; }
_ = interval.tick() => { /* periodic work */ }
}
}
```
## Related skills
- Use `skills/rust/rust-debugging` for GDB/LLDB debugging of async Rust programs
- Use `skills/rust/rust-profiling` for cargo-flamegraph with async stack frames
- Use `skills/low-level-programming/cpp-coroutines` for C++20 coroutine comparison
- Use `skills/low-level-programming/memory-model` for memory ordering in async contexts
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.